




UpperCAmelCase_		:					Tuple          =  0  # The first color of the flag.
UpperCAmelCase_		:					Any          =  1  # The second color of the flag.
UpperCAmelCase_		:					str          =  2  # The third color of the flag.
UpperCAmelCase_		:					Tuple          =  (red, white, blue)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if not sequence:
								return []
				if len(lowerCamelCase ) == 1:
								return list(lowerCamelCase )
				__magic_name__						:						int										=0
				__magic_name__						:						str										=len(lowerCamelCase ) - 1
				__magic_name__						:						Optional[Any]										=0
				while mid <= high:
								if sequence[mid] == colors[0]:
												__magic_name__					,			__magic_name__						:						Tuple										=sequence[mid], sequence[low]
												low += 1
												mid += 1
								elif sequence[mid] == colors[1]:
												mid += 1
								elif sequence[mid] == colors[2]:
												__magic_name__					,			__magic_name__						:						Optional[Any]										=sequence[high], sequence[mid]
												high -= 1
								else:
												__magic_name__						:						Optional[int]										=F"The elements inside the sequence must contains only {colors} values"
												raise ValueError(lowerCamelCase )
				return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					List[Any]          =  input("Enter numbers separated by commas:\n").strip()
							UpperCAmelCase_		:					Optional[int]          =  [int(item.strip()) for item in user_input.split(",")]
							print(F"""{dutch_national_flag_sort(unsorted)}""")





"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
